4446 results about "Risk identification" patented technology

The invention discloses an auditing decision support system and method based on a dynamic knowledge graph, relates to the technical field of computers, and aims to solve the problems that auditing data are heterogeneous and complex, risk identification is not timely and causal interpretation is lacked. According to the system, multi-modal audit data is collected in real time through a streaming event processing framework, and a dynamic audit knowledge graph with timeliness weight is constructed. Based on a graph calculation engine and cross-domain rule mining, identifying a high-frequency risk mode, and generating a risk conduction path graph; further fusing a multi-modal graph attention network, identifying and positioning abnormal entities, and outputting abnormal nodes and risk links thereof; and finally, the abnormal node embedding representation is dynamically updated through the time sequence diagram attention network, an interpretable audit causal map is generated in combination with a structural causal model, and closed-loop support from data acquisition and risk identification to interpretive audit decision is realized. The intellectualization and transparency of audit decision making are improved, and an efficient and traceable decision making basis is provided for a complex audit scene.

The invention discloses a battery pack thermal runaway risk identification and early warning system and method, and relates to the related field of battery risk early warning, and the method comprises the steps: collecting an operation parameter data set of a battery pack in real time through a multi-mode sensor network, carrying out the hybrid anomaly detection of the battery pack, obtaining a real-time anomaly detection result, and carrying out the thermal state prediction of the battery pack, and constructing a thermal state evolution trend chart to perform thermal runaway risk assessment, determining a thermal runaway risk level, triggering a multi-level early warning signal according to the thermal runaway risk level, and generating a thermal runaway suppression strategy. The technical problems that in existing battery thermal runaway early warning, due to monitoring limitation of a single sensor, the false alarm rate is high, response lags behind, and battery aging cannot be dynamically adapted are solved, and the technical effects that the thermal runaway early symptom detection rate is increased through multi-mode sensing, and the early warning time is shortened are achieved.

The invention discloses an underground engineering geological safety dynamic risk assessment method based on multi-source data fusion, which relates to the technical field of risk assessment, and comprises the following steps: collecting multi-source heterogeneous data related to underground engineering, extracting implicit information, modeling underground engineering geological safety risk factors into a risk network, and establishing a risk network model; calculating the comprehensive importance of the nodes based on a Stacking integration algorithm, and identifying key risk factors; acquiring characteristic parameters of key risk factors by using spatio-temporal characteristics of implicit information, introducing a random walk mechanism to acquire a dynamic accident prediction chain, and performing learning representation by using a graph attention network to acquire probability distribution of an accident evolution path; and assessing the vulnerability of the connection edge in the risk network, establishing a dynamic risk assessment model based on the node importance and the edge vulnerability, and obtaining a dynamic risk value corresponding to the accident according to the accident occurrence probability and the risk mitigation factor. According to the invention, intelligent identification, dynamic evaluation and accurate early warning of risk factors are realized, and the accuracy and real-time performance of risk identification and evaluation are improved.

The invention provides a knowledge graph generation method and system for science and technology project risk control, and the method comprises the steps: obtaining a multi-source heterogeneous data set of a target science and technology project, converting structured index data into a standard vector sequence through a heterogeneous data fusion mechanism, and converting unstructured text data into a semantic vector sequence; converting the time sequence behavior data into a behavior pattern vector sequence, inputting the three into a risk quantitative evaluation model, generating a risk entity feature matrix and a risk association strength matrix, and determining a node distribution topology of the knowledge graph according to entity feature vectors in the risk entity feature matrix; and according to association strength values in the risk association strength matrix, determining an entity relationship topology of the knowledge graph, generating a dynamic knowledge graph of the target science and technology project, and identifying a potential risk propagation path in the dynamic knowledge graph. According to the invention, the risk identification result has the dynamic characteristic of real-time updating, and the traceability of the multi-dimensional risk characteristic is maintained.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention discloses a load flow calculation and simulation control method and system for a digital twin power grid, and relates to the technical field of digital twin simulation control, and the method comprises the following steps: constructing a digital twin power grid model, and carrying out the dynamic topology optimization processing of the digital twin power grid model based on remote signaling credibility weighting; according to the optimized digital twin power grid model, identifying the power grid operation risk based on an integrated learning model; according to the risk identification result, generating a transfer path control strategy based on an analytic hierarchy process and a fuzzy comprehensive evaluation method; mapping the transfer path control strategy into a control action instruction set, and simulating execution and establishing a feedback correction mechanism on the digital twin power grid model; by generating the optimal path control strategy and performing control strategy analog simulation and self-adaptive feedback correction based on the digital twin power grid model, the problem of lack of intelligent path control strategy selection and simulation verification based on state dynamic identification in the prior art is solved.

The invention discloses a safety production risk identification method and system based on a knowledge graph, and relates to the technical field of safety production risk identification. Entity nodes and relation edge data of the knowledge graph are obtained, feature vectors are extracted, and embedded representation is generated by adopting a graph neural network; calculating a node weight by using an attention mechanism to determine a risk mode, traversing an association path to generate a risk propagation sequence, fusing time sequence features to update an entity state and determine a dynamic propagation path, extracting a key node sub-graph to adjust an edge weight to optimize the risk mode, and finally integrating environment features through iterative query to identify a complete risk propagation chain. According to the invention, dynamic tracking of equipment, personnel and environment network risks and cross-dimension cascade risk identification are realized.

The invention discloses a distribution cable branch box state monitoring method based on state recognition, and particularly relates to the technical field of power monitoring, and the method comprises the steps: collecting multi-dimensional operation data of a plurality of branch boxes in a continuous time period, and constructing a state evolution sequence; calculating a state offset score and an adjacent equipment state consistency score to judge whether an evaluation process is triggered or not; after triggering, constructing a state influence probability map, identifying an abnormal influence path, extracting a state disturbance diffusion index and a cooperative behavior deviation index, inputting into a pre-trained risk identification model, generating a risk level interval and a cause probability distribution vector, executing an influence regulation and control measure, and updating a state identification logic; according to the method, dynamic perception of the state of the branch box is realized by constructing a state evolution sequence, combined judgment of individual and group behaviors is realized by combining a state offset score and an adjacent equipment state consistency score, and a risk level interval and cause probability distribution vector are used for driving regulation and control strategies and map updating. And the monitoring precision and the self-adaptive capability of the system are improved.

The invention relates to the technical field of data management, in particular to an intelligent supervision method and system based on big data, and the method comprises the following steps: S1, multi-source data collection: collecting multi-source data in real time; s2, data preprocessing and spatio-temporal feature extraction: preprocessing the collected multi-source data, and extracting spatio-temporal features from the preprocessed multi-source data; s3, performing multi-dimensional risk assessment based on spatial-temporal characteristics: performing risk assessment on behaviors and states of the supervision objects by applying a multi-dimensional risk assessment algorithm; s4, dynamic early warning: in combination with a preset early warning rule, automatically triggering dynamic early warning, and providing a corresponding risk coping suggestion; and S5, supervision strategy optimization: optimizing and updating a supervision strategy through a self-learning mechanism based on a risk assessment result and feedback of dynamic early warning. According to the invention, the accuracy and response speed of risk identification are improved, so that the supervision work is more flexible and efficient.

The invention relates to the technical field of construction progress monitoring, and discloses a construction progress monitoring method and system based on big data. The method comprises the following steps: forming a space-time alignment data set through multi-source data acquisition, filtering and quality evaluation; performing feature extraction and registration to generate a digital model; target detection classification is performed to form a completion state table; progress evaluation is achieved through component-task mapping; trend analysis and risk identification are performed to generate a prediction result; decision reference is provided for personalized information screening and augmented reality display. Through multi-source data acquisition, fusion and intelligent analysis, accurate perception, objective evaluation, scientific prediction and visual presentation of the actual state of the construction site are realized, so that a comprehensive, accurate and prospective construction progress monitoring method is provided, the construction period delay risk is effectively reduced, and the construction management efficiency is improved.

The invention discloses a dangerous environment identification method based on a visual language model and a dynamic scene, particularly relates to the technical field of intelligent risk early warning, and is used for solving the problems of misjudgment and early warning lag caused by lack of event causal chain modeling in the dynamic scene in existing risk identification. According to the method, visual data flow and language description data are fused, a target motion track and a scene change sequence are extracted, a causal logic constraint set is constructed, space-time relevance is verified, a dangerous event causal chain is generated after high-confidence candidate events are screened, and finally a dynamic recognition result is generated based on causal chain overall risk quantification. Through dynamic rule adaptation and causal chain evolution rule matching, the random coexistence event and the real causal relationship are effectively distinguished, the problem of misjudgment caused by static rule stiffness in a traditional method is solved, and the reliability and the disposal timeliness of danger early warning in a complex dynamic scene are improved in combination with a global risk assessment mechanism.

The invention discloses a key infrastructure risk identification method and device in a flood disaster chain scene. The device comprises an acquisition module used for acquiring multi-source data and performing time-space alignment and fusion processing; the extraction module is used for extracting spatio-temporal evolution characteristics of a flood disaster chain based on the multi-source data; the construction module is used for constructing a key infrastructure coefficient according to the prior infrastructure information; the coupling module is used for coupling the spatio-temporal evolution characteristics and the key infrastructure coefficient, and calculating a regional risk index through a dynamic coupling coefficient and a space sensitivity factor; and the early warning module is used for generating a risk space distribution map and triggering dynamic early warning. According to the technical scheme, through multi-source data collection and space-time fusion, the space-time evolution characteristics of the flood disaster chain are extracted, key infrastructure coefficients based on information of buildings, roads, population and the like are constructed, the regional risk index is calculated through dynamic coupling, risk distribution is visually reflected, accurate early warning and emergency response are achieved, and the reliability of the system is improved. The urban disaster prevention and reduction level is effectively improved, and the public safety guarantee efficiency is remarkably enhanced.

The invention discloses a grassroots power supply enterprise compliance risk intelligent system and method based on big data analysis. The data acquisition unit is used for acquiring business operation data, historical violation records and policy and regulation update data of basic power supply enterprises to form a unified compliance data set. And the natural language processing unit performs text word segmentation and correlation analysis on the policy and regulation and violation record data, extracts key risk factors and labels compliance risk labels. And the risk feature construction unit performs multi-dimensional feature fusion on the business operation data and the compliance risk label data to generate a feature matrix for risk identification. And the intelligent risk assessment unit performs real-time analysis on the feature matrix by using a pre-trained machine learning model, identifies compliance risk categories and levels, and generates early warning information. According to the method, the accuracy of compliance risk identification is improved by using big data analysis and an intelligent algorithm, the compliance management cost of basic-level power supply enterprises is reduced, and the operation safety and compliance of the enterprises are improved.

The invention discloses a nursing risk intervention decision-making system and method based on a knowledge graph, and relates to the technical field of medical informationization, and the method comprises the steps: building a dynamic medical nursing knowledge graph based on multi-source nursing data and a deep learning model, and carrying out the entity recognition through a bidirectional long-short term memory network and a conditional random field; capturing evolution characteristics of an entity relationship through a marginal attention mechanism and a time sequence diagram convolutional network, and optimizing a knowledge graph structure based on connectivity constraints and a relationship weight threshold; a knowledge structure is dynamically adjusted by using a strengthened learning driven graph optimization mechanism, the relationship weight between entities is optimized in real time, a multi-level risk representation model is constructed, and accurate recognition and early warning of nursing risks are realized through local risk feature extraction, global risk information aggregation and time sequence risk evolution analysis. According to the method, the complex association relationship between the medical entities and the nursing risk decision are deeply fused, and the multi-dimensional medical entity features are constructed into the association network between the entities, so that early risk identification is realized.

The invention relates to the technical field of risk management and early warning, and discloses a risk early warning method comprising the following steps: collecting multi-source data including structured data, unstructured data and time series data; the invention provides a risk early warning system, which is used for performing cleaning, standardization processing and fusion on multi-source data to generate a unified data set for analysis, and comprises a data acquisition module used for acquiring the multi-source data including structured data, unstructured data and time sequence data; the data processing module is used for carrying out cleaning, standardization processing and fusion on the multi-source data and generating a unified data set, the electronic equipment comprises a memory, a processor and a communication module, and the processor implements the risk early warning method during execution. Through multi-source data fusion, a dynamic threshold model and deep learning analysis, the risk is accurately identified, the method adapts to the dynamic environment in real time, false alarms and missing alarms are reduced, and the long-term monitoring adaptability is improved.

The invention relates to a power construction monitoring method, system and device and a medium, and the method comprises the steps: carrying out the space-time calibration and anti-interference processing through collected construction images, temperature and dust data, and carrying out the fusion to generate a multi-source fusion data set containing environment information; performing feature extraction on the multi-source fusion data set based on a neural network model, synchronously identifying an action track of a constructor and equipment operation parameters, and constructing a feature map set associated with behaviors and equipment states; performing action and equipment anomaly conjoint analysis on the feature map set, and generating a construction risk event set by analyzing human body joints and temperature feature detection; performing spatial distance calculation and time monitoring on the risk event, and quantifying the coupling risk level; and based on the dynamic risk threshold, a grading early warning instruction is triggered, and precise response of field personnel warning, equipment power failure and evacuation guidance is realized. According to the method, the technical problems of low construction risk identification precision, equipment anomaly detection lagging and risk assessment deficiency in a complex environment are solved.

The invention discloses a grading, zoning and differentiating risk early warning method for mountain torrent disasters in medium and small watershed. Firstly, a drainage basin risk area is determined through mountain torrent disaster data analysis; the drainage basin delay time is calculated, the section water level and the drainage basin submerging range are simulated through the simplified hydrodynamic model, and quantitative analysis is conducted on the model; according to the method, a high-precision grid database is formed by combining unstructured grid division and risk source identification, and then clustering grading is performed on multi-dimensional characteristics of grids through a Gaussian mixture model (GMM) to obtain risk levels of the grids in a drainage basin. Finally, a differential early warning threshold value and a response strategy are formulated according to a grid partition grading result, and dynamic updating and linkage issuing are achieved on a GIS platform in combination with actual measurement data. According to the method, limited real conditions of small and medium-sized basin data and precision requirements of model simulation are taken into consideration, fine setting of the early warning threshold value is realized on the basis of grading and partitioning, and the accuracy of mountain torrent risk identification is also remarkably improved.

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

The invention discloses a special equipment life cycle supervision method and system based on characteristic parameter monitoring, and the method comprises the steps: collecting a multi-dimensional characteristic parameter data flow containing a real-time operation parameter, an accumulated damage parameter and a performance degradation parameter, and carrying out the trend analysis through employing a time sequence prediction model, and generating a trend deterioration early warning signal; an association rule mining algorithm is adopted to carry out association analysis to generate an associated fault early warning signal, then the two early warning signals are fused, inherent attribute data and historical operation and maintenance data are combined, and a real-time dynamic risk score is calculated through a dynamic risk portrait model; and finally mapping to a preset discrete supervision level and automatically executing a corresponding differential supervision instruction set. According to the method, the problems of risk identification lagging and strategy static solidification in traditional supervision are effectively solved, the transformation from passive response to active early warning and from average supervision to accurate strategy implementation is realized, and the foreseeability, pertinence and resource configuration efficiency of special equipment safety supervision are remarkably improved.

The invention relates to the technical field of prediction and alarm, in particular to a school computer room data operation monitoring and early warning method and system, and the method comprises the following steps: obtaining real-time data, such as CPU utilization rate, memory occupancy rate and network delay, through a sensor, generating a parameter sequence after standardization, extracting a difference sequence through a sliding window, and recognizing the output trend risk of a continuous rising interval. And extracting a behavior frequency to generate an abnormal coefficient, carrying out weighted analysis on the coupling degree to obtain an early warning threshold, and generating an early warning instruction if the growth rate exceeds the threshold and lasts for three time slices. According to the method, difference characteristics are extracted through a standardized time sequence and a sliding window, behavior state association is analyzed in combination with a periodic frequency, coupling degree dynamic early warning is calculated, a continuous deviation trend is screened, a real-time threshold range is formed, abnormal confusion is reduced, occupation of irrelevant alarm resources is reduced, risk identification capability is improved, and stable operation of equipment is guaranteed. And hidden danger accumulation occurrence probability is reduced.

The invention discloses an engineering project supervision real-time visual decision-making platform based on multi-source heterogeneous data fusion, and relates to the technical field of engineering supervision. The visual decision platform is in communication connection with a data acquisition module, a project risk feature module, a risk identification model construction module, a real-time data stream processing module, a risk early warning module and a decision support module. According to the invention, the risk in the engineering project is accurately identified in combination with historical multi-source heterogeneous data and project risk characteristics, and meanwhile, the real-time data stream processing module can analyze data in real time, identify potential risks and problems and generate early warning information, so that the accuracy and timeliness of risk identification are greatly improved, and the risk identification efficiency is improved. According to the invention, powerful decision support is provided for supervisors, and through the preset early warning level and the early warning signal, managers can rapidly understand the risk condition and take corresponding measures, thereby effectively reducing the project risk.

The invention relates to the technical field of building construction, in particular to a building construction safety intelligent early warning system based on multi-sensor fusion and deep learning. Comprising a multi-source sensing unit; an intelligent fusion unit; a depth analysis unit; and a dynamic response unit. According to the method, through a mixed deep learning model, personnel-equipment-environment space association in a 1m * 1m * 0.5 m space grid is extracted through an improved U-Net network, and a space risk association map is output; modeling data of 10 sampling periods by using a bidirectional LSTM network, and outputting a short-term prediction value; and carrying out weighted fusion through an attention mechanism to form a risk feature vector, and removing invalid anomalies in cooperation with parameter anomaly judgment and cross validation. And then a risk grade evaluation module introduces multiple coefficients to calculate a risk grade index, and a grid diffusion range is delimited according to grades, so that real-time identification, quantitative evaluation and range pre-judgment of construction safety risks are realized, and the problem that risk identification evaluation lacks scenarized accuracy and comprehensiveness is solved.

The invention provides a security risk assessment method and system for digital assets, and the method comprises the steps: obtaining a unique identifier and an ownership record of the digital assets, and determining key points of risk assessment, the key points comprising encrypted storage, transaction history and a circulation mechanism of the digital assets; for each key point, identifying identity authentication, access control and data encryption security factors in the key point, and performing security compliance verification on privacy protection and compliance requirements of the digital assets by adopting a probability statistical method; generating a plurality of digital asset cross-chain compatibility and value evaluation factor combinations through a Monte Carlo simulation method, simulating security events and consequences thereof in different scenes, and calculating the occurrence probability and loss influence of each scene; and according to the optimized risk assessment model, assessing the overall security risk of the digital assets, identifying the key link with the highest risk, and assessing the potential loss influence of the key link.

The invention relates to the technical field of thermal power generation, in particular to a thermal power plant fuel intelligent supervision system and method based on big data analysis and early warning, and the system comprises a multi-modal data sensing module, a hierarchical enhanced decision module, a real-time early warning and evaluation unit, and a digital twinborn decision center. Wherein the multi-modal data sensing module is used for constructing a fuel digital twinborn body; the hierarchical enhanced decision module is used for constructing a double-ring intelligent decision system and performing hierarchical optimization and full life cycle management; the real-time early warning and evaluation unit is used for acquiring data, performing deep mining, risk identification and dynamic adjustment of an early warning threshold in combination with a reinforcement learning algorithm, and grading the risks; the digital twinborn decision center performs virtual deduction by means of a digital twinborn model, generates a target strategy through a multi-target optimization algorithm, ensures instruction traceability, and dynamically adjusts the strategy according to real-time data. Therefore, the problems of limited data processing capability, low model adaptability and the like in the prior art are solved.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention discloses a data acquisition method and system and a storage medium, and relates to the technical field of data acquisition and processing.According to the technical scheme, multiple sensor devices and data interfaces are integrated, multi-source data are acquired in real time, format standardization processing, time synchronization correction and spatial information alignment are carried out through a multi-mode fusion module, and the data acquisition efficiency is improved. Calculating to obtain a data consistency factor Tyhz and evaluating the data consistency factor Tyhz; when the data consistency factor Tyhz does not reach the standard, a data optimization module performs noise filtering and abnormal value elimination on a multi-source data set, and an intelligent analysis module calculates a risk prediction parameter Fcyz by using a convolutional neural network; the early warning evaluation module compares the Fcyz with a risk evaluation threshold Fth, calculates a risk early warning index Gyzs, compares the risk early warning index Gyzs with a risk early warning threshold E, and dynamically generates an early warning execution scheme, so that information pushing and emergency resource scheduling are realized, the problems of low multi-source data fusion efficiency and insufficient early warning precision are solved, and the safety of the system is improved. And the risk identification and emergency response capabilities of the urban emergency management system are effectively improved.

The invention relates to the technical field of monitoring and early warning, in particular to a fire hydrant monitoring intelligent early warning system based on an anomaly analysis technology, which comprises a flow velocity anomaly identification module, a node collaborative pressure difference detection module, a pressure difference trend independence judgment module, a node degradation feature extraction module and a risk level generation module. According to the method, through correlation judgment of flow velocity deviation and control signals, no-signal recognition of abnormal water taking behaviors and a cooperative analysis mechanism of pressure change of adjacent nodes, a hydraulic disturbance area under non-manual control can be accurately recognized, and through analysis of spatial independence of a pressure response trend, a water flow disturbance area under non-manual control can be accurately recognized. The function degradation level of the device is extracted according to historical data of on-off time delay and response performance, the quantitative evaluation capability of the node function state is enhanced, the capability of finely dividing the risk level is achieved when risk early warning is given out, the risk identification accuracy is improved, and the active discovery capability of early fault hidden dangers is enhanced.

The invention discloses an intelligent contract risk recognition method and system, and relates to the technical field of text recognition, and the method comprises the steps: extracting a semantic vector of a to-be-recognized file; obtaining a first risk identification result based on the semantic vector and the review list; performing semantic matching on the semantic vector and the review knowledge graph to obtain a potential risk; obtaining a first risk category based on the potential risk and the review knowledge spectrogram, obtaining a preset risk judgment rule based on the first risk category, and obtaining a second risk category based on the preset risk judgment rule; obtaining a second risk identification result based on the contract category and the second risk category; constructing a clause rule base, and obtaining a compliance result based on the semantic vector and the clause rule base; obtaining a complete result based on the semantic vector and the standardized contract template library; and obtaining a total risk identification result based on the above identification result, thereby solving the problems of low risk identification efficiency and low accuracy caused by the fact that an existing contract term risk identification method depends on the determination of the license experience of professionals.

The invention provides a tunnel construction safety monitoring and early warning method and system based on multi-dimensional data fusion, and relates to the technical field of construction safety early warning, and the method comprises the steps: obtaining first information which comprises tunnel construction parameters and image information; performing space-time fusion processing and redundancy reduction processing on the first information to generate a four-dimensional hypergraph tensor space of tunnel construction; performing cross-scale analysis and topology extraction on the four-dimensional hypergraph tensor space, and constructing a dynamic heterogeneous graph network; performing space-time diagram convolution processing on the dynamic heterogeneous graph network, and generating a composite risk manifold based on a result obtained by processing and a multi-head attention mechanism; coupling processing is carried out according to the composite risk manifold, stability analysis and critical state judgment are carried out based on the risk phase change hypersurface obtained through processing, and real-time early warning is carried out based on a dynamic early warning boundary of tunnel construction obtained through judgment. According to the invention, the risk identification accuracy and the early warning response timeliness in the tunnel construction environment are improved.

The invention discloses a reservoir bank slope geological disaster risk identification method based on an unmanned aerial vehicle laser radar, and relates to the technical field of reservoir bank slope prevention and control, an unmanned aerial vehicle is used for carrying laser radar equipment, flight scanning is carried out on a reservoir bank slope area, and three-dimensional point cloud data is obtained. According to the reservoir bank slope identification method based on the unmanned aerial vehicle laser radar detection data, high-precision terrain and geological information of the reservoir bank slope is rapidly acquired in a non-contact manner by using the three-dimensional imaging capability of the unmanned aerial vehicle laser radar technology, so that the data acquisition efficiency and safety are greatly improved; the method also remarkably improves the precision and integrity of data, accurately captures key topographic factors such as topographic relief, gradient, slope direction and the like through laser radar scanning, and provides a solid foundation for the accurate recognition of bank slope types in combination with geological factors such as geological structures, lithology and the like, so that the prediction and evaluation of bank slope disasters are more accurate. Preventive measures can be taken in advance, and disaster risks are reduced.